Memristive devices are electrical switches that can alter their state of internal resistance based on an applied voltage and current. See, for example, Yang et al., “Memristive Devices for Computing: Mechanisms, Applications and Challenges,” USLI Process Integration 8 at the 224th Electrochemical Society Meeting (Oct. 27-Nov. 1, 2013) (7 pages). Memristive devices have gained significant interest for accelerated machine learning applications.
Memristive devices need to have the following characteristics. Memristive devices have to be non-volatile and capable of storing a variable resistance value. This resistance can be tuned up and down using current or voltage pulses. Memristive device resistance needs to be symmetrically tunable, meaning that when positive (+) or negative (−) voltage pulses are applied to the device, the resistance moves up or down by roughly the same magnitude.
Current nanoionic memristor devices (e.g., resistive random-access memory (RRAM), conductive bridging random access memory (CBRAM), etc.) however do not show symmetric modulation between stored resistance states. This asymmetry precludes as is current technologies for use in the implementation of the resistive processing unit (RPU) for accelerated machine learning which involves performing a large number of computations in parallel.
Therefore, techniques for achieving symmetric modulation of resistances in a memristive device would be desirable.